In recent years, as environmental degradation has become more and more serious, the Chinese government has formulated a series of environmental policies and regulations aimed at improving environmental quality. Does environmental regulation significantly inhibit environmental pollution? Environmental regulation will not only directly affect environmental pollution but also have an indirect impact on environmental pollution. This paper uses Bayesian posterior probability, the optimal model structure selection method, based on join 112 kinds of spatial econometric model structure, and the panel data of 30 provinces in China from 2003 to 2016 to study the effects of environmental regulation on environmental pollution base on the industrial agglomeration mechanism of synergy effect. The research covers the national level and four regions, including the eastern, central, western, and northeastern regions of China. The research shows that: (1) environmental regulation at the national level and in the eastern, central and northeastern regions can significantly curb environmental pollution, but the environmental pollution in the western region shows a significant trend of enhancement. (2) Increased industrial agglomeration across China has significantly worsened environmental pollution. (3) Environmental regulation and industrial agglomeration form a significant synergy effect, which has a significant positive impact on environmental pollution in regions other than northeast China, and a significant negative impact on environmental pollution intensity in northeast China.

Energy is a foundation for a city to create economic wealth, satisfy people’s desires, and achieve benefits. However, the increasing mismatch between energy supply and demand and the worsening of environmental pollution have highlighted the importance of improving urban energy performance, so the number of studies related to urban energy performance evaluation is increasing. Based on describing the authors, numbers, regional sources, and themes of these studies, this paper reviews and analyzes the conceptions, evaluation indicators, influencing factors, evaluation methods, and evaluation systems related to urban energy performance. Most countries have expressed concern about this topic. Researchers in China, Belgium, and the USA have had the most achievements and collaborations. The concept of urban energy performance further extends to a comprehensive performance. It is measured based on an input-output process. In addition to the original evaluation indicators, new desirable outputs and undesirable outputs are included. Industrial structure, energy price, population density, home car ownership, climate factors, Gini coefficient, health expenditure level, and unemployment rate are regarded as influencing factors. Therefore, a new framework of evaluation indicators and influencing factors is constructed. Stochastic frontier analysis (SFA) and data envelopment analysis (DEA) are commonly used to evaluate. With changes in conceptions, evaluation indicators, and influencing factors, the evaluation method should rather focus on measuring multiple input-output variables, determining the evaluation results and the impacts of factors at the same analysis stage, and highlighting policy orientations. As an important management tool, the evaluation system would continue to be studied and developed.

This paper represents a new proposed trade model of “Intercountries Trade Force (ITF)” which is inspired by Intermolecular Interaction Forces in chemical sciences, and has potential to compensate for the deficiencies of the gravity trade model proposed by Jan Tinbergen in 1962. The main differences between our new model and the earlier gravity trade theory are (i) there is a time-variant variable called the gravity index (GI) which means that the earlier gravity theory was treated as only a variable in our new proposed model and (ii) our new proposed trade model has a higher chance of adoption in the real trade world rather than the earlier gravity trade model which always needs to be expanded by scholars. In order to empirically test our new proposed trade model, we applied it in an empirical econometric model to analyze the Russian gas export to the EU member states, not explored earlier. Results revealed that our new trade proposed model adjusts with the empirical energy trade pattern.

The study was designed to evaluate the impact of differentiated technological innovation efficiencies of industrial enterprises on the local emissions of environmental pollutants in Anhui province, China, during the period of 2012–2016. The econometric models of DEA and SEM-PLS and Malmquist index are used to explore the potential impacts of differentiated technological innovation efficiencies of industrial enterprises on the local emissions of environmental pollutants in Anhui province. After an initial analysis of SEM-PLS model, the models of DEA and Malmquist index are used to evaluate the differentiated degrees and dynamic development levels of local technological innovation efficiencies of industrial enterprises in different regions of Anhui province. With these analyses, the study presents three main results as follows. There is a positive correlation between the technological innovation efficiencies of industrial enterprises and the technological performance levels of environmental disposal. Meanwhile, there is a large gap among the environmental disposing performances of industrial enterprises in different regions of Anhui province. There is also a large gap between the expected and actual technological performances of industrial enterprises’ environmental disposal, according to the results of SEM-PLS analysis. Furthermore, there are several obvious characteristics of geographical distribution in the impact of differentiated technological innovation efficiencies of industrial enterprises on local environmental pollutant emissions observed from the results of the DEA and Malmquist index models. However, it is not consistent with the overall provincial development trend and regional distribution pattern of industrial economics in Anhui province over the period of 2012–2016. Under the rapid development of social economics and modern technological advance, there is a weak impact of differentiated technological innovation efficiency on the technological performance of industrial environmental disposal in different regions of Anhui province. Meanwhile, the environmental disposal capacity of enterprises’ technological innovation become declining too. Finally, some countermeasures and policy suggestions are put forward based on the investigation and comprehensive analyses of the DEA and SEM-PLS and Malmquist index models.

This study involves the climate change impact assessment of wheat producers in Khyber Pakhtunkhwa, Pakistan. An extensive farm survey of 150 farms was designed. From study area, three districts, namely, Chitral, D.I. Khan, and Peshawar, were selected through multistage sampling process. Yield simulation from Crop model DSSAT (Decision Support System for Agro Technology Transfer) was used for socio-economic impact assessment. Future climate scenarios were generated by selecting five GCMs from latest CMIP5 family with two RCPs 4.5 and 8.5, at two carbon concentrations of 499 ppm and 571 ppm, respectively. Yield simulations were analyzed for each GCM. Results of crop model revealed that wheat yield will increase in district Chitral, while in D.I. Khan and Peshawar, yields would be reduced due to climate change. For socio-economic impact assessment, TOA-MD (Trade-Off Analysis for Multi-Dimensional Impact Assessment) version 6 was used. Climate change impacts on poverty, net farm returns, and per capita income were calculated for different scenarios. The analysis was carried out on per-farm basis. The economic model results revealed that climate change has negative impact on wheat producers in D.I. Khan and Peshawar while making wheat producers better off in Chitral. The number of losers ranged from 54 to 66.21% and 50 to 61.99% in D.I. Khan and Peshawar, respectively. Losers are the farmers who would be economically worse off under perturbed climate. With current climate, the observed poverty rate would be 34 to 49 in D.I. Khan while 21.26 to 34.03 in Peshawar. The study recommended need for adaptation strategies to overcome the vulnerabilities of climate change.

China’s industrial energy consumption accounted for approximately 70% of national energy demand in the past four decades. Regarding energy demand and environmental pollution, success in controlling energy demand and reducing energy intensity for industrial sectors in China would play a crucial role for the country’s sustainable growth problems. To formulate targeted energy plans, the features and characters of China’s industrial energy intensity should be carefully evaluated. In this study, a carefully designed econometric model that considers different technological factors including indigenous R&D and technology spillovers from foreign direct investment and trade under a united framework is applied to investigate the β-convergence characteristics for China’s industrial energy intensity by employing a panel dataset covering China’s 34 industrial sectors over 2000–2010. The results verify the existence of β-convergence in industrial energy intensity during the sample period. For the industrial sectors overall and the light industrial sectors, the empirical results indicate that indigenous R&D and technology spillovers from FDI and imports are beneficial in curbing energy intensity. However, technology spillover through exports makes it harder to reduce energy intensity. In addition, not all technological factors have played a significant role in reducing energy intensity for the heavy industrial sectors.

Combining with the spatial variations in environmental technological innovation cross-province in China, this study employs the spatial econometric model to explore how environmental technological innovation responds to changes in command-and-control regulation and three other traditional determinants (the environmental R&D investments, the environmental labor force inputs, and the provincial economic development level) during the study period from 2004 to 2016.The results indicate that there is a clear sign of spatial correlation in the environmental technological innovation according to the global and local indicators of spatial association. Then, considering the spatial interdependence of environmental technological innovation and holding other variables constant, evidence shows that command-and-control regulation has a significant adverse effect on environmental technological innovation in the whole country and the Eastern region, while the effect is statistically positive in the Western region and non-significant in the Central region. We also find that the effects in two sub-periods of 2004–2010 and 2011–2016 are obvious differences because some new command-and-control regulation instruments were formulated and implemented in 2010. In addition, environmental technological innovation is also directly catalyzed by the environmental R&D investments, the environmental labor force inputs, and the provincial economic development level.

Based on the idea of a “Community of Human Destiny,” the Chinese government proposed the “Belt and Road” initiative, and clearly proposed to promote green development and strengthen ecological environmental protection. However, a considerable number of countries are resource-rich economies with serious market misallocations. Using the stochastic frontier analysis (SFA) combined with the directional distance function (DDF) framework, this paper measures the green total factor productivity (GTFP) and its items (i.e., technical change and efficiency change) of 33 countries along the Belt and Road in 1995–2012, and then the impact of market misallocations on GTFP is analyzed. The following conclusions are drawn: (1) The main driving force for GTFP promotion in Asian countries came from technical change, while in European countries, it came from efficiency change. (2) Market misallocations had significantly hindered the GTFP of these economies. Countries with greater market misallocations have smaller GTFP. (3) Results based on counterfactual measures showed that GTFP could be increased by up to 4.04% and the average can be increased by 1.24% after eliminating market misallocations.